Maritime surveillance radars are critical in commerce, transportation, navigation, and defense. However, the sea environment is perhaps the most challenging of natural radar backdrops because maritime radars must contend with electromagnetic backscatter from the sea surface, or sea clutter. Sea clutter poses unique challenges in very low grazing angle geometries, where typical statistical assumptions regarding sea clutter backscatter do not hold. As a result, traditional constant false alarm rate (CFAR) detection schemes may yield a large number of false alarms while objects of interest may be challenging to detect. Solutions posed in the literature to date have been either computationally impractical or lacked robustness. This dissertation explores whether fully polarimetric radar offers a means of enhancing detection performance in low grazing angle sea clutter. To this end, MIT Lincoln Laboratory funded an experimental data collection using a fully polarimetric X-band radar assembled largely from commercial off-the-shelf components. The Point de Chene Dataset, collected on the Atlantic coast of Massachusetts’ Cape Ann in October 2015, comprises multiple sea states, bandwidths, and various objects of opportunity. The dataset also comprises three different polarimetric transmit schemes. In addition to discussing the radar, the dataset, and associated post-processing, this dissertation presents a derivation showing that an established multiple input, multiple output radar technique provides a novel means of simultaneous polarimetric scattering matrix measurement. A novel scheme for polarimetric radar calibration using a single active calibration target is also presented. Subsequent research leveraged this dataset to develop Polarimetric Co-location Layering (PCL), a practical algorithm for mitigation of low grazing angle sea clutter, which is the most significant contribution of this dissertation. PCL routinely achieves a significant reduction in the standard CFAR false alarm rate while maintaining detections on objects of interest. Moreover, PCL is elegant: It exploits fundamental characteristics of both sea clutter and object returns to determine which CFAR detections are due to sea clutter. We demonstrate that PCL is robust across a range of bandwidths, pulse repetition frequencies, and object types. Finally, we show that PCL integrates in parallel into the standard radar signal processing chain without incurring a computational time penalty.

abstract: A critical problem for airborne, ship board, and land based radars operating in maritime or littoral environments is the detection, identification and tracking of targets against backscattering caused by the roughness of the sea surface. Statistical models, such as the compound K-distribution (CKD), were shown to accurately describe two separate structures of the sea clutter intensity fluctuations. The first structure is the texture that is associated with long sea waves and exhibits long temporal decorrelation period. The second structure is the speckle that accounts for reflections from multiple scatters and exhibits a short temporal decorrelation period from pulse to pulse. Existing methods for estimating the CKD model parameters do not include the thermal noise power, which is critical for real sea clutter processing. Estimation methods that include the noise power are either computationally intensive or require very large data records. This work proposes two new approaches for accurately estimating all three CKD model parameters, including noise power. The first method integrates, in an iterative fashion, the noise power estimation, using one-dimensional nonlinear curve fitting, with the estimation of the shape and scale parameters, using closed-form solutions in terms of the CKD intensity moments. The second method is similar to the first except it replaces integer-based intensity moments with fractional moments which have been shown to achieve more accurate estimates of the shape parameter. These new methods can be implemented in real time without requiring large data records. They can also achieve accurate estimation performance as demonstrated with simulated and real sea clutter observation datasets. The work also investigates the numerically computed Cram\'er-Rao lower bound (CRLB) of the variance of the shape parameter estimate using intensity observations in thermal noise with unknown power. Using the CRLB, the asymptotic estimation performance behavior of the new estimators is studied and compared to that of other estimators. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

<p>A helicopter based system equipped with a millimetre-wave fire control radar is studied for improvement of the detection probability for moving ground targets. The system is a pulse doppler radar that sends out the electromagnetic wave in repetitive pulses. The frequency in which radar sends this pulse is called Pulse Repetition Frequency (PRF). </p><p>The velocity of the moving targets is measured by spectral analysis of the pulses received. With this sort of system some targets will not be possible to detect. These targets are said to be blind for the radar. Also the measured targets can be ambiguous. A solution to this is to shift PRF during the time the radar emits itspulses on the target. This method is called PRF block staggering. The antenna is mounted on a mechanical sweep on top of the rotor of the helicopter. Because of this the time on target is limited. This means that the measured velocity resolution of the radar is also limited. </p><p>With the studied method it is possible to resolve range and velocity ambiguities. This is under the condition that it is acceptable with a reduction in the velocity resolution by a factor equal to the number of PRF blocks used. </p><p>The work leading to this thesis included developing a program in Mathworks Matlab for studying different configurations of PRFs and the effect on the detection probability.</p>

Could you tell me how many marketing messages you've seen or heard since you woke up this morning? Probably the answer is no. Messages stemming from advertising, promotions, PR, direct marketing, and salesmen are everywhere. We encounter them on our way to work, in newspapers and magazines, stores and restaurants, when watching our favorite sports, looking out of an airplane window, or even while socializing with friends. As a consumer, it is impossible to keep track of everything marketers are trying to tell us. This abundance of marketing messages is often referred to as clutter. Clutter makes consumers less likely to pay attention to marketing messages. What is more, clutter breeds avoidance behavior and skepticism, which makes consumers more likely to actively resist marketing messages. This thesis sets out to further our understanding of marketing communications in a cluttered environment. The thesis comprises five articles in which issues of clutter are empirically investigated. Specifically, conventional wisdoms regarding slogans, media choice, and PR are put to test in more ecologically valid settings than what has been used in previous research. By including these settings, our understanding of marketing communications in the presence of competing messages is improved. Whereas clutter to date is mostly considered a challenge for advertising, the thesis broadens the perspective by showing that clutter is a challenge to, and consequence of, all types of marketing communications. By offering a framework for understanding clutter, the thesis provides support for marketers trying to make sense of message competition and what it means for their marketing communications. It also provides ideas of how to face the clutter challenge – or even benefit from it. / Diss. Stockholm : Handelshögskolan, 2008 Sammanfattning jämte 5 uppsatser

Airborne CW doppler proximity sensors are very sensitive, but leaks precise height measurement. It may be possible to estimate the heigth at the terminal phase (the case where the sensor is at a heigth close to ground) precisely by using the doppler shift and amplitude information. The thesis includes this novel concept with theoritical analysis and simulation results.

In airborne radar systems with Ground Moving Target Indication (GMTI) mode, it is desired to detect the presence of targets in the interference consisting of noise, ground clutter, and jamming signals. These interference components usually mask the target return signal, such that the detection requires suppression of the interference signals. Space-time adaptive processing is a widely used interference suppression technique which uses temporal and spatial information to eliminate the effects of clutter and jamming and enables the detection of moving targets with small radial velocity. However, adaptive estimation of the interference requires high computation capacity as well as large secondary sample data support. The available secondary range cells may be fewer than required due to non-homogeneity problems and computational capacity of the radar system may not be sufficient for the computations required. In order to reduce the computational load and the required number of secondary data for estimation, parametric methods use a priori information on the structure of the clutter covariance matrix. Space Time Auto-regressive (STAR) filtering, which is a parametric adaptive method, and full parametric model-based approaches for interference suppression are proposed as alternatives to STAP in the literature. In this work, space time auto-regressive filtering and model-based GMTI approaches are investigated. Performance of these approaches are evaluated by both simulated and flight test data and compared with the performance of sample matrix inversion space time adaptive processing.

A helicopter based system equipped with a millimetre-wave fire control radar is studied for improvement of the detection probability for moving ground targets. The system is a pulse doppler radar that sends out the electromagnetic wave in repetitive pulses. The frequency in which radar sends this pulse is called Pulse Repetition Frequency (PRF). The velocity of the moving targets is measured by spectral analysis of the pulses received. With this sort of system some targets will not be possible to detect. These targets are said to be blind for the radar. Also the measured targets can be ambiguous. A solution to this is to shift PRF during the time the radar emits itspulses on the target. This method is called PRF block staggering. The antenna is mounted on a mechanical sweep on top of the rotor of the helicopter. Because of this the time on target is limited. This means that the measured velocity resolution of the radar is also limited. With the studied method it is possible to resolve range and velocity ambiguities. This is under the condition that it is acceptable with a reduction in the velocity resolution by a factor equal to the number of PRF blocks used. The work leading to this thesis included developing a program in Mathworks Matlab for studying different configurations of PRFs and the effect on the detection probability.

Conventional fixed threshold detectors set a fixed threshold based on the overall statistical characteristics of the spatially uniform clutter over all ranges to give a specific probability of false alarm and detection. However, in radar applications clutter statistics are not known a priori. Constant False Alarm Rate (CFAR) techniques provide an adaptive threshold to estimate the clutter statistics and to distinguish targets from clutter. In Cell Averaging CFAR (CA-CFAR) the threshold is controlled by averaging the fixed size CFAR cells surrounding the cell under test. In this thesis, radar detection of targets in sea clutter modelled by compound Kdistribution is examined from a statistical detection viewpoint by Monte Carlo simulations. The performance of CA-CFAR processors is analysed under varying conditions of sea clutter spatial correlation and spikiness for several cases of false alarm probability, the length of cell size used in the CFAR processor and the number of pulses integrated prior to CA-CFAR processor. v The detection performance of CA-CFAR is compared with the performance of fixed threshold detection. The performance evaluations are quantified by CFAR loss. CFAR loss is defined as the increase in average signal to clutter ratio compared to that of fixed threshold, required to achieve a given probability of detection and probability of false alarm. Curves for CFAR loss to the spikiness and spatial correlation of clutter, number of pulses integrated and the length of cell size are presented.

La multiplication des conflits de part le monde a eu pour principale conséquence de disséminer des millions de mines antipersonnel qui mettent en danger la vie des populations et constituent une entrave au développement économique des régions concernées. Dans ce mémoire, de nouvelles techniques de traitement du signal sont proposées pour la détection des mines antipersonnel dans les données enregistrées par un GPR (Ground Penetrating Radar). Deux filtres numériques sont tout d'abord conçus pour réduire le clutter, qui constitue un ensemble de phénomènes indésirables, dans les données de type Bscan et Cscan fournies par le GPR. Ces deux types de données représentent respectivement des images de tranches verticales et horizontales du sous-sol. La conception des filtres nécessite une modélisation géométrique du clutter et d'une signature de mine, pour chaque type de données, suivie d'une analyse spectrale permettant de définir le gabarit du filtre idéal. Ensuite, une nouvelle méthode de détection, basée sur une technique de détection de ruptures non paramétrique, est proposée afin de localiser automatiquement les réponses des mines antipersonnel sur des données Bscan. La méthode consiste à rechercher les ruptures spatiales suivant la direction des mesures afin de détecter les positions horizontales des mines et les ruptures suivant l'axe temporel afin de détecter les temps de réponse des mines. Une méthode de détection, basée sur l'extraction de contours fermés, est également proposée pour localiser les réponses des mines sur des données Cscan. Les performances de ces deux méthodes de détection sont évaluées par le calcul de probabilité de détection et de fausses alarmes.

With increasing information density and complexity, computer displays may become visually cluttered, adversely affecting overall usability. Text labels can significantly add to visual clutter in graphical user interfaces, but are generally kept legible through specific label placement algorithms that seek visual separation of labels and other objects in the 2D view plane. This work studies an alternative approach: can overlapping labels be visually segregated by distributing them in stereoscopic depth? The fact that we have two forward-looking eyes yields stereoscopic disparity: each eye has a slightly different perspective on objects in the visual field. Disparity is used for depth perception by the human visual system, and is therefore also provided by stereoscopic 3D displays to produce a sense of depth. This work has shown that a stereoscopic label placement algorithm yields user performance comparable with existing algorithms that separate labels in the view plane. At the same time, such stereoscopic label placement is subjectively rated significantly less disturbing than traditional methods. Furthermore, it does not allow for potentially ambiguous spatial relationships between labels and background objects inherent to labels separated in the view plane. These findings are important for display systems where disturbance, distraction and ambiguity of the overlay can negatively impact safety and efficiency of the system, including the reference application of this work: an augmented vision system for Air Traffic Control towers.